Designing a machine vision system for a mobile robot to detect and mark dangerous areas

There is no doubt that machine vision systems offer many benefits in many applications, as they improve the ability of machines to adapt and learn. When implementing a new application it is necessary to design a vision system that matches the requirements of the application, as there is a wide range of parameters that must be considered during the design. Our goal in this paper is to learn about these different parameters and define the different requirements for designing a machine vision system for a mobile robot, whose task is to examine different environments autonomously, detect hazardous materials, and mark high-risk areas, in various weather conditions and around the clock

Distribución de tamaño y composición química de los aerosoles presentes en áreas urbanas en Colombia

ilustraciones, fotografías a color, mapas a colorThe Colombian Air Quality National Strategy have established that prevention actions, reduction, and control emissions should be focused mainly on particulate matter, PM10 and PM2.5, due to the consistent exceedances related to the air quality guidelines suggest by the World Health Organization and the Air Quality National Standard in urban areas. In addition, the harmful effects in public health of population exposed to airborne particles. Therefore, one of the aims of the Strategy is encouraging the scientific knowledge to improve the national air quality management. Therefore, the aim of this dissertation's is to further knowledge of particle size distribution (PSD) of aerosols in urban areas in Colombian, linking the local and regional sources of air pollutants and the local weather patterns. The study was conducted in two places of Colombia: Bogotá and Palmira (Cauca’s Valley). The airborne aerosols were investigated utilizing impactors cascades to measure the size of inhale particles. The particle number concentration and particle number size distribution were measured in the size range from 17 nm and 10 mm using an Electrical Low-Pressure Impactor (ELPI+). Also, was used an Andersen Non-viable Impactor Cascade to determinate the mass size distribution and collect samples size segregated in nine stages between 0.1 to 9 um, to determine the contained of organic carbon, elemental carbon, and water-soluble ions in airborne inhalable particles. The findings of this study revealed that Bogotá's urban background area had a higher average particle number concentration (3800 #/cm3) than the area that was most adversely affected by automobile emissions (2800 x103 #/cm3). The number particle size distribution was unimodal in the traffic station of “Las Ferias” with a diameter centered in 120 nm, which evidences the particles are formed and grow through atmospheric process. In other hand, the urban background area exhibited a bimodal distribution, with a larger mode centered in particles of 120 nm of diameter with a second mode centered in 30 nm of diameter, which is more relevant in morning rush traffic hours. The Lung Deposition Surface Area (LDSA) was estimated from the interaction between particle size distribution and the model of the particle deposition in the respiratory system published by the Commission on Radiological Protection (IRCP) (ICRP, 1994). In this study, the urban background area reveled higher concentrations than the area affected by traffic emissions in Bogota, and other similar environments reported in the scientific literature. The submicrometric particles PM1, which can enter the alveolar region of the human respiratory system, was 20.8 ug/m3 in Bogota and 13.8 ug/m3 in Palmira. The mass size distribution exhibits a bimodal distribution that is equally centered between 0.43 and 1.1 um and 4.7 and 9.0 um. According to the chemical composition size separated, elemental carbon was accumulated in the fine fraction of PM2.1 in Bogotá at a rate of 72%, relative to 57% in Palmira. On the other hand, organic carbon was more evenly distributed in fine and coarse fraction. The sulfate ion was one of the most abundant water-soluble ions in two sites, but the size distribution was different while in Palmira was mainly accumulated in fine mode, in Bogota was dispersed across the two-size fraction. (Texto tomado de la fuente)La Estrategia Nacional de Calidad del Aire de Colombia ha establecido que las acciones de prevención, reducción y control de emisiones deben estar enfocadas principalmente a las emisiones de material particulado, PM10 y PM2.5, debido a las excedencias con respecto a los lineamientos de calidad del aire sugeridos por la Organización Mundial de la Salud y la Norma Nacional de Calidad del Aire. Además de los efectos nocivos en la salud pública de la población expuesta a partículas dispersas en el aire. De allí que, uno de los objetivos de la Estrategia es fomentar el conocimiento científico para mejorar la gestión de la calidad del aire a nivel nacional. El objetivo de esta tesis es contribuir en el conocimiento de la distribución del tamaño de partículas (PSD) de los aerosoles en áreas urbanas de Colombia, vinculando las fuentes locales y regionales de contaminantes del aire y las condiciones meteorológicas locales. Este estudio se realizó en dos lugares de Colombia: Bogotá y Palmira (Valle del Cauca). Los aerosoles en el aire se investigaron utilizando impactadores en cascada para medir el tamaño de las partículas inhaladas. La concentración del número de partículas y la distribución del tamaño del número de partículas se midieron en el rango de tamaño de 17 nm y 10 um usando un Impactador Eléctrico de Baja Presión (ELPI+). Adicionalmente, se utilizó un impactador en cascada Non-Viable Andersen para determinar la distribución de tamaño en masa y recolectar muestras segregadas en nueve etapas entre 0.1 y 9 um, para determinar el contenido de carbono orgánico, carbono elemental e iones solubles en agua de las partículas inhalables. Los hallazgos de este estudio revelaron que el área de fondo urbano de Bogotá tenía una concentración promedio de partículas más alta (3800 #/cm3) que el área más afectada por las emisiones de los automóviles (2800 #/cm3). La distribución del tamaño de partículas en número fue unimodal en la estación de calidad del aire “Las Ferias”, principalmente afectada por emisiones de tráfico vehicular, con un diámetro centrado en 120 nm, lo que evidenció que las partículas se forman y crecen a través de procesos atmosféricos del entorno. Por otro lado, el área de fondo urbano exhibió una distribución bimodal, con un diámetro modal de 120 nm y un segundo modo centrada en 30 nm de diámetro, la cual es más relevante en las horas pico de la mañana. El Área de superficie de deposición pulmonar (LDSA) se estimó a partir de la interacción entre la distribución del tamaño de las partículas y el modelo de deposición de partículas en el sistema respiratorio publicado por la Comisión de Protección Radiológica (IRCP) (ICRP, 1994). En este estudio, el área de fondo urbano mostro concentraciones más altas que el área afectada por las emisiones de tráfico en Bogotá, y otros ambientes similares reportados en la literatura científica. Las partículas submicrométricas PM1, que pueden ingresar a la región alveolar del sistema respiratorio humano, fue de 20,8 ug/m3 en Bogotá y 13,8 ug/m3 en Palmira. La distribución de tamaño de masa exhibe una distribución bimodal que está igualmente centrada entre 0.43 y 1.1 um, y 4.7 y 9.0 um. De acuerdo con la composición química separada por tamaño, el carbono elemental se acumuló en la fracción fina de PM2.1 en Bogotá a una tasa del 72%, frente al 57% en Palmira. Por otro lado, el carbono orgánico se distribuyó más uniformemente en la fracción fina y gruesa. El ion sulfato fue uno de los iones solubles en agua más abundantes en dos sitios, pero la distribución de tamaños fue diferente, mientras que en Palmira se acumuló principalmente en modo fino, en Bogotá se dispersó en la fracción de dos tamaños.DoctoradoDoctor en IngenieríaCiencias Atmosférica

Hybrid genetic algorithm and particle filter optimization model for simultaneous localization and mapping problems

Determining position of a robot and knowing position of the required objects on the map in unknown environments such as underwater, other planets and the remaining areas of natural disasters has led to the development of efficient algorithms for Simultaneous Localization and Mapping (SLAM). The current solutions for solving the SLAM have some drawbacks. For example, the solutions based on Extended Kalman Filter (EKF) are faced with limitation in non-linear models and non-Gaussian errors which are causes for decrease of accuracy. The solutions based on particle filter are also suffering from high memory complexity and time complexity. One of the major approaches to solve the SLAM problem is the approach based on Evolutionary Algorithm (EA). The main advantage of the EA is that it can be used in search space which is too large to be used with high convergence while its disadvantage is high time and computational complexity. This thesis proposes two optimization models in solving SLAM problem namely Hybrid Optimization Model (HOM) and Lined-Based Genetic Algorithm Optimization Model (LBGAOM). These models do not have the limitations of EKF, memory complexity of particle filter, and disadvantages of EA in search space. When the results of HOM compared with original EA, it showed an increase of accuracy based on presented fitness function. The best fitness in original EA was 16.36 but in HOM has reached to 16.68. Both models applied a proposed new representation model. The representation model is designed and used to represent the robot and its environment and is based on occupancy grid and genetic algorithm. There are two types of representation models proposed in this thesis namely Layer 1 and Layer 2. For each layer, related fitness function is created to evaluate the accuracy of map in the model that was tested with some different parameters. The proposed HOM is designed based on genetic algorithm and particle filter by creating a new mutation model inspired by particle filter. The search space is reduced and only suitable space will be explored based on proposed functions. The proposed LBGAOM is a new optimization model based on extraction line from laser sensor data to increase the speed. In this model, search space in the map is a set of lines instead of pixel by pixel and it makes searching time faster. The evaluation of the proposed representation model shows that Layer 2 has better fitness value than Layer 1. The HOM has better performance compared to original GA Layer 1. The LBGAOM has decreased the search space compared to pixel based model. In conclusion, the proposed optimization models have good performance in solving the SLAM problem in terms of speed and accuracy